Package 'methylKit'

Description

Measured 5mC levels via bisulfite sequencing might be a combination of 5hmC and 5mC levels since bisulfite sequencing can not distinguish between the two. This function can adjust 5mC levels of a bisulfite sequencing experiment if the user supplies corresponding 5hmC levels from the same sample.

Usage

adjustMethylC(mc,hmc,save.db,...,chunk.size)

## S4 method for signature 'methylRaw,methylRaw'
adjustMethylC(mc, hmc, save.db = FALSE, ..., chunk.size = 1e+06)

## S4 method for signature 'methylRawList,methylRawList'
adjustMethylC(mc, hmc, save.db = FALSE, ..., chunk.size = 1e+06)

## S4 method for signature 'methylRawDB,methylRawDB'
adjustMethylC(mc, hmc, save.db = TRUE, ..., chunk.size = 1e+06)

## S4 method for signature 'methylRawListDB,methylRawListDB'
adjustMethylC(mc, hmc, save.db = TRUE, ..., chunk.size = 1e+06)

Arguments

Value

returns adjusted 5-methyl cytosine levels in the form of methylRawList, methylRaw, methylRawDB or methylRawListDB object depending on the input object

Details

The parameter chunk.size is only used when working with methylRawDB or methylRawListDB objects, as they are read in chunk by chunk to enable processing large-sized objects which are stored as flat file database. Per default the chunk.size is set to 1M rows, which should work for most systems. If you encounter memory problems or have a high amount of memory available feel free to adjust the chunk.size.

The parameter save.db is per default TRUE for methylDB objects as methylRawDB and methylRawListDB, while being per default FALSE for methylRaw and methylRawList. If you wish to save the result of an in-memory-calculation as flat file database or if the size of the database allows the calculation in-memory, then you might change the value of this parameter.

References

1. Booth, Branco, et al. (2012). Quantitative Sequencing of 5-Methylcytosine and 5-Hydroxymethylcytosine at Single-Base Resolution. Science, 934

2. Yu, Hon, et al. (2012). Base-resolution analysis of 5-hydroxymethylcytosine in the Mammalian genome. Cell, 149(6), 1368-80.

Examples

# read 5hmC and 5mC files
hmc.file=system.file("extdata", "test1.myCpG.txt", package = "methylKit")
mc.file =system.file("extdata", "test2.myCpG.txt", package = "methylKit")

my5hmC=methRead( hmc.file,sample.id="hmc",assembly="hg18")
my5mC =methRead( mc.file,sample.id="mc",assembly="hg18")

# adjusting the 5mC levels using 5hmC levels
adjusted.5mC=adjustMethylC(my5mC,my5hmC)

Description

This function associates principal components with sample annotations such as age, gender, batch_id. Can be used to detect which batch effects are associated with the variation in the methylation values.

Usage

assocComp(mBase, sampleAnnotation)

Arguments

Value

a named list of principal component matrix (named 'pcs'), by principal compopents (named 'vars') and a p-value matrix showing association p-values between sample annotations and principal components (named 'association').

Author(s)

Altuna Akalin

Examples

data(methylKit)
sampleAnnotation=data.frame(batch_id=c("a","a","b","b"),age=c(19,34,23,40))
as=assocComp(mBase=methylBase.obj,sampleAnnotation)

Description

The function converts methylRaw, methylRawDB, methylRawList, methylRawListDB, methylDiff or methylDiffDB object into a bedgraph format. It either writes as a file or returns a data.frame

Usage

bedgraph(
  methylObj,
  file.name = NULL,
  col.name,
  unmeth = FALSE,
  log.transform = FALSE,
  negative = FALSE,
  add.on = "",
  chunk.size = 1e+06
)

## S4 method for signature 'methylDiff'
bedgraph(
  methylObj,
  file.name,
  col.name,
  unmeth,
  log.transform,
  negative,
  add.on
)

## S4 method for signature 'methylRaw'
bedgraph(
  methylObj,
  file.name,
  col.name,
  unmeth,
  log.transform,
  negative,
  add.on
)

## S4 method for signature 'methylRawList'
bedgraph(
  methylObj,
  file.name,
  col.name,
  unmeth,
  log.transform,
  negative,
  add.on
)

## S4 method for signature 'methylRawDB'
bedgraph(
  methylObj,
  file.name = NULL,
  col.name,
  unmeth = FALSE,
  log.transform = FALSE,
  negative = FALSE,
  add.on = "",
  chunk.size = 1e+06
)

## S4 method for signature 'methylRawListDB'
bedgraph(
  methylObj,
  file.name = NULL,
  col.name,
  unmeth = FALSE,
  log.transform = FALSE,
  negative = FALSE,
  add.on = "",
  chunk.size = 1e+06
)

## S4 method for signature 'methylDiffDB'
bedgraph(
  methylObj,
  file.name,
  col.name,
  log.transform,
  negative,
  add.on,
  chunk.size
)

Arguments

Value

Returns a data.frame or list of data.frames if file.name=NULL, if a file.name given appropriate bed file will be written to that file

Details

The parameter chunk.size is only used when working with methylRawDB, methylRawListDB or methylDiffDB objects, as they are read in chunk by chunk to enable processing large-sized objects which are stored as flat file database. Per default the chunk.size is set to 1M rows, which should work for most systems. If you encounter memory problems or have a high amount of memory available feel free to adjust the chunk.size.

Examples

data(methylKit)

# getting a bedgraph file from a methylDiff object containing differential 
# methylation percentages
bedgraph(methylDiff.obj, file.name="test.bed", col.name="meth.diff", 
         unmeth=FALSE,log.transform=FALSE,negative=FALSE,add.on="")

# remove the file
unlink("test.bed")

# getting a bedgraph file from a methylBase object containing percent 
#methylation values
bedgraph(methylRawList.obj[[1]], file.name="test2.bed", col.name="perc.meth", 
         unmeth=FALSE,log.transform=FALSE,negative=FALSE,add.on="")

unlink("test2.bed") # remove the file

Description

The function calculates differential methylation statistics between two groups of samples. The function uses either logistic regression test or Fisher's Exact test to calculate differential methylation. See the rest of the help page and references for detailed explanation on statistics.

Usage

calculateDiffMeth(
  .Object,
  covariates = NULL,
  overdispersion = c("none", "MN", "shrinkMN"),
  adjust = c("SLIM", "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr",
    "none", "qvalue"),
  effect = c("wmean", "mean", "predicted"),
  parShrinkMN = list(),
  test = c("F", "Chisq", "fast.fisher", "midPval"),
  mc.cores = 1,
  slim = TRUE,
  weighted.mean = TRUE,
  chunk.size = 1e+06,
  save.db = FALSE,
  ...
)

## S4 method for signature 'methylBaseDB'
calculateDiffMeth(
  .Object,
  covariates = NULL,
  overdispersion = c("none", "MN", "shrinkMN"),
  adjust = c("SLIM", "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr",
    "none", "qvalue"),
  effect = c("wmean", "mean", "predicted"),
  parShrinkMN = list(),
  test = c("F", "Chisq", "fast.fisher", "midPval"),
  mc.cores = 1,
  slim = TRUE,
  weighted.mean = TRUE,
  chunk.size = 1e+06,
  save.db = TRUE,
  ...
)

Arguments

Value

a methylDiff object containing the differential methylation statistics and locations for regions or bases

Details

Covariates can be included in the analysis. The function will then try to separate the influence of the covariates from the treatment effect via a linear model.
The Chisq-test is used per default only when no overdispersion correction is applied. If overdispersion correction is applied, the function automatically switches to the F-test. The Chisq-test can be manually chosen in this case as well, but the F-test only works with overdispersion correction switched on.
If there is one sample in each group, e.g. after applying the pooling samples, the Fisher's exact test will be applied for differential methylation. methyKit offers two implementations to perform this test, which yield slightly different results but differ much in computation time. "fast.fisher" is a cut down version 'fisher.test()' that should produce the exact same results as the base implementation, while "midPval" will produce marginaly different p-values, but offers a large boost in calculation speed.

The parameter chunk.size is only used when working with methylBaseDB objects, as they are read in chunk by chunk to enable processing large-sized objects which are stored as flat file database. Per default the chunk.size is set to 1M rows, which should work for most systems. If you encounter memory problems or have a high amount of memory available feel free to adjust the chunk.size.

The parameter save.db is per default TRUE for methylDB objects as methylBaseDB, while being per default FALSE for methylBase. If you wish to save the result of an in-memory-calculation as flat file database or if the size of the database allows the calculation in-memory, then you might want to change the value of this parameter.

References

Altuna Akalin, Matthias Kormaksson, Sheng Li, Francine E. Garrett-Bakelman, Maria E. Figueroa, Ari Melnick, Christopher E. Mason. (2012). "methylKit: A comprehensive R package for the analysis of genome-wide DNA methylation profiles." Genome Biology.

McCullagh and Nelder. (1989). Generalized Linear Models. Chapman and Hall. London New York.

Barnard. (1989). On alleged gains in power from lower P-values. Statistics in Medicine. Armitage and Berry. (1994) Statistical Methods in Medical Research (3rd edition). Blackwell.

See Also

pool, reorganize dataSim

Examples

data(methylKit)

# The Chisq-test will be applied when no overdispersion control is chosen.
my.diffMeth=calculateDiffMeth(methylBase.obj,covariates=NULL,
                              overdispersion=c("none"),
                              adjust=c("SLIM"))

# pool samples in each group
pooled.methylBase=pool(methylBase.obj,sample.ids=c("test","control"))
 
# After applying the pool() function, there is one sample in each group.
# The Fisher's exact test will be applied for differential methylation.
my.diffMeth2=calculateDiffMeth(pooled.methylBase,covariates=NULL,
                               overdispersion=c("none"),
                               adjust=c("SLIM"))
                               
# Covariates and overdispersion control:
# generate a methylBase object with age as a covariate
covariates=data.frame(age=c(30,80,30,80))
sim.methylBase<-dataSim(replicates=4,sites=1000,treatment=c(1,1,0,0),
                        covariates=covariates,
                        sample.ids=c("test1","test2","ctrl1","ctrl2"))

# Apply overdispersion correction and include covariates 
# in differential methylation calculations.
my.diffMeth3<-calculateDiffMeth(sim.methylBase,
                                covariates=covariates,
                                overdispersion="MN",test="Chisq",mc.cores=1)

Description

This function provides an interface to the beta-binomial model from DSS package by Hao Wu. It calculates the differential methylation statistics using a beta-binomial model with parameter shrinkage. See the reference for details.

Usage

calculateDiffMethDSS(
  meth,
  adjust = c("SLIM", "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr",
    "none", "qvalue"),
  mc.cores = 1
)

## S4 method for signature 'methylBase'
calculateDiffMethDSS(
  meth,
  adjust = c("SLIM", "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr",
    "none", "qvalue"),
  mc.cores = 1
)

Arguments

Value

a methylDiff object

References

Feng H, Conneely K and Wu H (2014). A bayesian hierarchical model to detect differentially methylated loci from single nucleotide resolution sequencing data. Nucleic acids research

Examples

data(methylKit)

dssDiffay <- calculateDiffMethDSS(methylBase.obj, adjust="SLIM", mc.cores=1)

Hierarchical Clustering using methylation data The function clusters samples using hclust function and various distance metrics derived from percent methylation per base or per region for each sample.

Description

Hierarchical Clustering using methylation data

The function clusters samples using hclust function and various distance metrics derived from percent methylation per base or per region for each sample.

Usage

clusterSamples(.Object, dist="correlation", method="ward",
                       sd.filter=TRUE,sd.threshold=0.5,
                       filterByQuantile=TRUE, plot=TRUE,chunk.size)

## S4 method for signature 'methylBase'
clusterSamples(
  .Object,
  dist,
  method,
  sd.filter,
  sd.threshold,
  filterByQuantile,
  plot
)

## S4 method for signature 'methylBaseDB'
clusterSamples(
  .Object,
  dist = "correlation",
  method = "ward",
  sd.filter = TRUE,
  sd.threshold = 0.5,
  filterByQuantile = TRUE,
  plot = TRUE,
  chunk.size = 1e+06
)

Arguments

Value

a tree object of a hierarchical cluster analysis using a set of dissimilarities for the n objects being clustered.

Details

The parameter chunk.size is only used when working with methylBaseDB objects, as they are read in chunk by chunk to enable processing large-sized objects which are stored as flat file database. Per default the chunk.size is set to 1M rows, which should work for most systems. If you encounter memory problems or have a high amount of memory available feel free to adjust the chunk.size.

Examples

data(methylKit)

clusterSamples(methylBase.obj, dist="correlation", method="ward", plot=TRUE)

Description

The function simulates DNA methylation data from multiple samples. See references for detailed explanation on statistics.

Usage

dataSim(
  replicates,
  sites,
  treatment,
  percentage = 10,
  effect = 25,
  alpha = 0.4,
  beta = 0.5,
  theta = 10,
  covariates = NULL,
  sample.ids = NULL,
  assembly = "hg18",
  context = "CpG",
  add.info = FALSE
)

Arguments

Value

a methylBase object containing simulated methylation data, or if add.info=TRUE a list containing the methylbase object and the indices of all treated sites (differentially methylated bases or regions) as the second element.

Details

The function uses a Beta distribution to simulate the methylation proportion background across all samples. The parameters alpha, beta used in a beta distribution to draw methylation proportions,$\mu$, from a typical bimodal distribution. For each initial methylation proportion drawn using the parameters above, a range of methylation proportions is distributed around the original $\mu$ with overdispersion parameter $\theta$, this is using an alternative parameterization of Beta distribution: $Beta(\mu,\theta)$. The parameters percentage and effect determine the proportion of sites that are affected by the treatment (meaning differential sites) and the strength of this influence, respectively. effect is added on top of $\mu$ for the CpGs that are affected by the treament. The affected group of samples for that particular CpG will now be distributed by $Beta(\mu+effect,\theta)$. The coverage is modeled with a negative binomial distribution, using rnbinom function with size=1 and prob=0.01. The additional information needed for a valid methylBase object, such as CpG start, end and strand, is generated as "dummy values", but can be overwritten as needed.

Examples

data(methylKit)

# Simulate data for 4 samples with 20000 sites each.
# The methylation in 10% of the sites are elevated by 25%.
my.methylBase=dataSim(replicates=4,sites=2000,treatment=c(1,1,0,0),
percentage=10,effect=25)

Description

This function gets number of hyper/hypo methylated regions/bases from methylDiff object. It can also plot percentages of differentially methylated bases per chromosome.

Usage

diffMethPerChr(
  x,
  plot = TRUE,
  qvalue.cutoff = 0.01,
  meth.cutoff = 25,
  exclude = NULL,
  keep.empty.chrom = FALSE,
  ...
)

## S4 method for signature 'methylDiff'
diffMethPerChr(
  x,
  plot = TRUE,
  qvalue.cutoff = 0.01,
  meth.cutoff = 25,
  exclude = NULL,
  keep.empty.chrom = FALSE,
  ...
)

## S4 method for signature 'methylDiffDB'
diffMethPerChr(
  x,
  plot = TRUE,
  qvalue.cutoff = 0.01,
  meth.cutoff = 25,
  exclude = NULL,
  keep.empty.chrom = FALSE,
  ...
)

Arguments

Value

plots a piechart or a barplot for percentage of the target features overlapping with annotation

Examples

data(methylKit)
 
# get a list of differentially methylated bases/regions per chromosome and overall
diffMethPerChr(methylDiff.obj, plot=FALSE,qvalue.cutoff=0.01, 
               meth.cutoff=25,exclude=NULL)

Description

The function extracts part of the data and returns a new object.

Usage

## S4 method for signature 'methylRaw,ANY,ANY,ANY'
x[i, j]

## S4 method for signature 'methylBase,ANY,ANY,ANY'
x[i, j]

## S4 method for signature 'methylDiff,ANY,ANY,ANY'
x[i, j]

## S4 method for signature 'methylRawDB,ANY,ANY,ANY'
x[i, j]

## S4 method for signature 'methylBaseDB,ANY,ANY,ANY'
x[i, j]

## S4 method for signature 'methylDiffDB,ANY,ANY,ANY'
x[i, j]

Arguments

Examples

data(methylKit)

# selects first hundred rows, returns a methylRaw object
subset1=methylRawList.obj[[1]][1:100] 

# selects first hundred rows, returns a methylBase object
subset2=methylBase.obj[1:100,] 

# selects first hundred rows, returns a methylDiff object
subset3=methylDiff.obj[1:100,] 

# This will get chromomsomes, will return a factor
# That means the resulting object will ceases to be a methylKit object
chrs=methylDiff.obj[[2]]

Description

This function filters methylRaw, methylRawDB, methylRawList and methylRawListDB objects. You can filter based on lower read cutoff or high read cutoff. Higher read cutoff is usefull to eliminate PCR effects Lower read cutoff is usefull for doing better statistical tests.

Usage

filterByCoverage(
  methylObj,
  lo.count = NULL,
  lo.perc = NULL,
  hi.count = NULL,
  hi.perc = NULL,
  chunk.size = 1e+06,
  save.db = FALSE,
  ...
)

## S4 method for signature 'methylRaw'
filterByCoverage(
  methylObj,
  lo.count = NULL,
  lo.perc = NULL,
  hi.count = NULL,
  hi.perc = NULL,
  chunk.size = 1e+06,
  save.db = FALSE,
  ...
)

## S4 method for signature 'methylRawList'
filterByCoverage(
  methylObj,
  lo.count = NULL,
  lo.perc = NULL,
  hi.count = NULL,
  hi.perc = NULL,
  chunk.size = 1e+06,
  save.db = FALSE,
  ...
)

## S4 method for signature 'methylRawDB'
filterByCoverage(
  methylObj,
  lo.count = NULL,
  lo.perc = NULL,
  hi.count = NULL,
  hi.perc = NULL,
  chunk.size = 1e+06,
  save.db = TRUE,
  ...
)

## S4 method for signature 'methylRawListDB'
filterByCoverage(
  methylObj,
  lo.count = NULL,
  lo.perc = NULL,
  hi.count = NULL,
  hi.perc = NULL,
  chunk.size = 1e+06,
  save.db = TRUE,
  ...
)

Arguments

Value

methylRaw, methylRawDB, methylRawList or methylRawListDB object depending on input object

Details

The parameter chunk.size is only used when working with methylRawDB or methylRawListDB objects, as they are read in chunk by chunk to enable processing large-sized objects which are stored as flat file database. Per default the chunk.size is set to 1M rows, which should work for most systems. If you encounter memory problems or have a high amount of memory available feel free to adjust the chunk.size.

The parameter save.db is per default TRUE for methylDB objects as methylRawDB and methylRawListDB, while being per default FALSE for methylRaw and methylRawList. If you wish to save the result of an in-memory-calculation as flat file database or if the size of the database allows the calculation in-memory, then you might change the value of this parameter.

Examples

data(methylKit)

# filter out bases with covereage above 500 reads
filtered1=filterByCoverage(methylRawList.obj,lo.count=NULL,lo.perc=NULL,
hi.count=500,hi.perc=NULL)

# filter out bases with cread coverage above 99.9th percentile of coverage
# distribution
filtered2=filterByCoverage(methylRawList.obj,lo.count=NULL,lo.perc=NULL,
hi.count=NULL,hi.perc=99.9)

# filter out bases with covereage above 500 reads and save to database 
# "test1_max500.txt.bgz" 
# in directory "methylDB", filtered3 now becomes a \code{methylRawDB} object
filtered3=filterByCoverage(methylRawList.obj[[1]], lo.count=NULL,lo.perc=NULL, 
                           hi.count=500, hi.perc=NULL, save.db=TRUE, 
                           suffix="max500", dbdir="methylDB")
                           
# tidy up
rm(filtered3)
unlink("methylDB",recursive=TRUE)

Description

The function returns the genome assembly stored in any of the methylBase,methylBaseDB,methylRaw, methylRawDB,methylDiff objects

Usage

getAssembly(x)

## S4 method for signature 'methylBase'
getAssembly(x)

## S4 method for signature 'methylRaw'
getAssembly(x)

## S4 method for signature 'methylDiff'
getAssembly(x)

## S4 method for signature 'methylRawDB'
getAssembly(x)

## S4 method for signature 'methylBaseDB'
getAssembly(x)

## S4 method for signature 'methylDiffDB'
getAssembly(x)

Arguments

Value

the assembly string for the object

Examples

data(methylKit)

getAssembly(methylBase.obj)
getAssembly(methylDiff.obj)
getAssembly(methylRawList.obj[[1]])

Description

The function returns the context of methylation. For example: "CpG","CHH" or "CHG"

Usage

getContext(x)

## S4 method for signature 'methylBase'
getContext(x)

## S4 method for signature 'methylRaw'
getContext(x)

## S4 method for signature 'methylDiff'
getContext(x)

## S4 method for signature 'methylRawDB'
getContext(x)

## S4 method for signature 'methylBaseDB'
getContext(x)

## S4 method for signature 'methylDiffDB'
getContext(x)

Arguments

Value

a string for the context methylation

Examples

data(methylKit)

getContext(methylBase.obj)
getContext(methylDiff.obj)
getContext(methylRawList.obj[[1]])

Description

The functions returns a matrix of correlation coefficients and/or a set of scatterplots showing the relationship between samples. The scatterplots will contain also fitted lines using lm() for linear regression and lowess for polynomial regression.

Usage

getCorrelation(object,method="pearson",plot=FALSE,nrow)

## S4 method for signature 'methylBase'
getCorrelation(object, method = c("pearson", "kendall", "spearman"), plot)

## S4 method for signature 'methylBaseDB'
getCorrelation(object, method = "pearson", plot = FALSE, nrow = 2e+06)

Arguments

Value

a correlation matrix object and plot scatterPlot

Details

The argument 'nrow' is only evaluated if the input is a methylBaseDB object. If 'nrow' is not specified getCorrelation will read the first 2M records of the given object, but if you want to read all records 'nrow' has to be NULL. You should change 'nrow' if using getCorrelation with all records of the methylBaseDB object would take too long.

If the scatter plot is plotted, the red line in the plot is from linear regression fit and the green line is from polynomial regression fit with stats::lowess.

Examples

data(methylKit)

getCorrelation(methylBase.obj,method="pearson",plot=FALSE)

# create methylBaseDB
methylBaseDB.obj <- unite(methylRawList.obj,save.db=TRUE,dbdir="methylDB")

getCorrelation(methylBaseDB.obj,method="pearson",plot=FALSE,nrow=10000)




# remove Database again
rm(methylBaseDB.obj)
unlink("methylDB",recursive=TRUE)

Description

The function returns basic statistics about read coverage per base. It can also plot a histogram of read coverage values.

Usage

getCoverageStats(
  object,
  plot = FALSE,
  both.strands = FALSE,
  labels = TRUE,
  ...,
  chunk.size = 1e+06
)

## S4 method for signature 'methylRaw'
getCoverageStats(
  object,
  plot = FALSE,
  both.strands = FALSE,
  labels = TRUE,
  ...,
  chunk.size = 1e+06
)

## S4 method for signature 'methylRawDB'
getCoverageStats(
  object,
  plot = FALSE,
  both.strands = FALSE,
  labels = TRUE,
  ...,
  chunk.size = 1e+06
)

Arguments

Value

a summary of coverage statistics or plot a histogram of coverage

Details

The parameter chunk.size is only used when working with methylRawDB or methylRawListDB objects, as they are read in chunk by chunk to enable processing large-sized objects which are stored as flat file database. Per default the chunk.size is set to 1M rows, which should work for most systems. If you encounter memory problems or have a high amount of memory available feel free to adjust the chunk.size.

Examples

data(methylKit)

# gets coverage stats for the first sample in methylRawList.obj object
getCoverageStats(methylRawList.obj[[1]],plot=TRUE,
both.strands=FALSE,labels=TRUE)

Description

The functions retrieves the table containing methylation information from methylKit Objects. The data retrived from this function is of a data.frame. This is basically containing all relevant methylation information per genomic region or base.

Usage

getData(x)

## S4 method for signature 'methylBase'
getData(x)

## S4 method for signature 'methylRaw'
getData(x)

## S4 method for signature 'methylDiff'
getData(x)

## S4 method for signature 'methylRawDB'
getData(x)

## S4 method for signature 'methylBaseDB'
getData(x)

## S4 method for signature 'methylDiffDB'
getData(x)

Arguments

Value

data frame for methylation events

Examples

data(methylKit)

# following commands show first lines of returned 
# data.frames from getData() function
head(
getData(methylBase.obj)
)

head( getData(methylDiff.obj))

head(getData(methylRawList.obj[[1]]))

Description

The function returns the path to the flat file database that stores the data of the methylRawDB, methylRawListDB, methylBaseDB or methylDiffDB objects.

Usage

getDBPath(x)

## S4 method for signature 'methylRawListDB'
getDBPath(x)

## S4 method for signature 'methylBaseDB'
getDBPath(x)

## S4 method for signature 'methylRawDB'
getDBPath(x)

## S4 method for signature 'methylDiffDB'
getDBPath(x)

Arguments

Examples

data(methylKit)

methylBaseDB.obj <- unite(methylRawList.obj,save.db=TRUE,dbdir="methylDB")


#The path to the database is returned
getDBPath(methylBaseDB.obj)


# remove Database again
rm(methylBaseDB.obj)
unlink("methylDB",recursive=TRUE)

Description

The function returns basic statistics about It can also plot a histogram of

Usage

getMethylationStats(
  object,
  plot = FALSE,
  both.strands = FALSE,
  labels = TRUE,
  ...,
  chunk.size = 1e+06
)

## S4 method for signature 'methylRaw'
getMethylationStats(
  object,
  plot = FALSE,
  both.strands = FALSE,
  labels = TRUE,
  ...,
  chunk.size = 1e+06
)

## S4 method for signature 'methylRawDB'
getMethylationStats(
  object,
  plot = FALSE,
  both.strands = FALSE,
  labels = TRUE,
  ...,
  chunk.size = 1e+06
)

Arguments

Value

a summary of Methylation statistics or plot a histogram of coverage

Details

The parameter chunk.size is only used when working with methylRawDB or methylRawListDB objects, as they are read in chunk by chunk to enable processing large-sized objects which are stored as flat file database. Per default the chunk.size is set to 1M rows, which should work for most systems. If you encounter memory problems or have a high amount of memory available feel free to adjust the chunk.size.

Examples

data(methylKit)

# gets Methylation stats for the first sample in methylRawList.obj object
getMethylationStats(methylRawList.obj[[1]],plot=TRUE,
both.strands=FALSE,labels=TRUE)

Description

The function subsets a methylDiff or methylDiffDB object in order to get differentially methylated bases/regions satisfying thresholds.

Usage

getMethylDiff(
  .Object,
  difference = 25,
  qvalue = 0.01,
  type = "all",
  chunk.size = 1e+06,
  save.db = FALSE,
  ...
)

## S4 method for signature 'methylDiff'
getMethylDiff(
  .Object,
  difference = 25,
  qvalue = 0.01,
  type = "all",
  chunk.size = 1e+06,
  save.db = FALSE,
  ...
)

## S4 method for signature 'methylDiffDB'
getMethylDiff(
  .Object,
  difference = 25,
  qvalue = 0.01,
  type = "all",
  chunk.size = 1e+06,
  save.db = TRUE,
  ...
)

Arguments

Value

a methylDiff or methylDiffDB object containing the differential methylated locations satisfying the criteria

Details

The parameter chunk.size is only used when working with methylDiffDB objects, as they are read in chunk by chunk to enable processing large-sized objects which are stored as flat file database. Per default the chunk.size is set to 1M rows, which should work for most systems. If you encounter memory problems or have a high amount of memory available feel free to adjust the chunk.size.

The parameter save.db is per default TRUE for methylDB objects as methylDiffDB, while being per default FALSE for methylDiff. If you wish to save the result of an in-memory-calculation as flat file database or if the size of the database allows the calculation in-memory, then you might want to change the value of this parameter.

Examples

data(methylKit)

# get differentially methylated bases/regions with specific cutoffs
all.diff=getMethylDiff(methylDiff.obj,difference=25,qvalue=0.01,type="all")

# get hyper-methylated
hyper=getMethylDiff(methylDiff.obj,difference=25,qvalue=0.01,type="hyper")

# get hypo-methylated
hypo=getMethylDiff(methylDiff.obj,difference=25,qvalue=0.01,type="hypo")

Description

The function returns or replaces the sample-ids stored in any of the following methylKit objects: methylRaw, methylRawDB, methylBase, methylBaseDB, methylRawList, methylRawListDB, methylDiff, methylDiffDB.

Usage

getSampleID(x)
getSampleID(x) <- value

getSampleID(x) <- value

## S4 method for signature 'methylRawList'
getSampleID(x)

## S4 replacement method for signature 'methylRawList'
getSampleID(x) <- value

## S4 method for signature 'methylBase'
getSampleID(x)

## S4 replacement method for signature 'methylBase'
getSampleID(x) <- value

## S4 method for signature 'methylRaw'
getSampleID(x)

## S4 replacement method for signature 'methylRaw'
getSampleID(x) <- value

## S4 method for signature 'methylDiff'
getSampleID(x)

## S4 replacement method for signature 'methylDiff'
getSampleID(x) <- value

## S4 method for signature 'methylRawListDB'
getSampleID(x)

## S4 replacement method for signature 'methylRawListDB'
getSampleID(x) <- value

## S4 method for signature 'methylBaseDB'
getSampleID(x)

## S4 replacement method for signature 'methylBaseDB'
getSampleID(x) <- value

## S4 method for signature 'methylRawDB'
getSampleID(x)

## S4 replacement method for signature 'methylRawDB'
getSampleID(x) <- value

## S4 method for signature 'methylDiffDB'
getSampleID(x)

## S4 replacement method for signature 'methylDiffDB'
getSampleID(x) <- value

Arguments

Examples

data(methylKit)

#The Sample-Ids can be printed ..
getSampleID(methylBase.obj)

# .. or replaced. 
newObj <- methylBase.obj
getSampleID(newObj) <- c("sample1","sample2","sample3","sample4")
getSampleID(newObj)

Description

The function returns or replaces the treatment vector stored in any of the following methylKit objects: methylBase,methylRawList,methylBaseDB, methylRawListDB,methylDiff,methylDiffDB.

Usage

getTreatment(x)
getTreatment(x) <- value

getTreatment(x) <- value

## S4 method for signature 'methylRawList'
getTreatment(x)

## S4 replacement method for signature 'methylRawList'
getTreatment(x) <- value

## S4 method for signature 'methylBase'
getTreatment(x)

## S4 replacement method for signature 'methylBase'
getTreatment(x) <- value

## S4 method for signature 'methylDiff'
getTreatment(x)

## S4 replacement method for signature 'methylDiff'
getTreatment(x) <- value

## S4 method for signature 'methylRawListDB'
getTreatment(x)

## S4 replacement method for signature 'methylRawListDB'
getTreatment(x) <- value

## S4 method for signature 'methylBaseDB'
getTreatment(x)

## S4 replacement method for signature 'methylBaseDB'
getTreatment(x) <- value

## S4 method for signature 'methylDiffDB'
getTreatment(x)

## S4 replacement method for signature 'methylDiffDB'
getTreatment(x) <- value

Arguments

Examples

data(methylKit)

# The treatment vector can be printed ..
getTreatment(methylBase.obj)
 
# .. or replaced with a new one  
newObj <- methylBase.obj
getTreatment(newObj) <- c(1,2,3,4)
getTreatment(newObj)

Description

Segmentation and clustering are two distinct steps in methSeg(), leading to adjacent segments of the same class. This leads to a bias segment length distributions, which is removed by joining those neighbours.

Usage

joinSegmentNeighbours(res)

Arguments

Value

A GRanges object with segment classification and information.

See Also

methSeg

Description

The function converts in-memory methylKit objects to methylDB objects

Usage

makeMethylDB(obj, dbdir = getwd())

## S4 method for signature 'methylBase'
makeMethylDB(obj, dbdir = getwd())

## S4 method for signature 'methylRaw'
makeMethylDB(obj, dbdir = getwd())

## S4 method for signature 'methylDiff'
makeMethylDB(obj, dbdir = getwd())

## S4 method for signature 'methylRawList'
makeMethylDB(obj, dbdir = getwd())

Arguments

Value

an methylBaseDB,methylRawDB, methylRawListDB or an methylDiffDB object

Examples

## Not run: 
data(methylKit)


makeMethylDB(methylBase.obj,"my/path")

## End(Not run)

Description

The function reads a list of files or single files with methylation information for bases/region in the genome and creates a methylrawList or methylraw object. The information can be stored as flat file database by creating a methylrawlistDB or methylrawDB object.

Usage

methRead(
  location,
  sample.id,
  assembly,
  dbtype = NA,
  pipeline = "amp",
  header = TRUE,
  skip = 0,
  sep = "\t",
  context = "CpG",
  resolution = "base",
  treatment = NA,
  dbdir = getwd(),
  mincov = 10
)

## S4 method for signature 'character,character,character'
methRead(
  location,
  sample.id,
  assembly,
  dbtype,
  pipeline,
  header,
  skip,
  sep,
  context,
  resolution,
  dbdir,
  mincov
)

## S4 method for signature 'character,ANY,ANY'
methRead(
  location,
  sample.id,
  assembly,
  dbtype,
  pipeline,
  header,
  skip,
  sep,
  context,
  resolution,
  dbdir,
  mincov
)

## S4 method for signature 'list,list,character'
methRead(
  location,
  sample.id,
  assembly,
  dbtype = NA,
  pipeline = "amp",
  header = TRUE,
  skip = 0,
  sep = "\t",
  context = "CpG",
  resolution = "base",
  treatment = NA,
  dbdir = getwd(),
  mincov = 10
)

## S4 method for signature 'list,ANY,ANY'
methRead(
  location,
  sample.id,
  assembly,
  dbtype = NA,
  pipeline = "amp",
  header = TRUE,
  skip = 0,
  sep = "\t",
  context = "CpG",
  resolution = "base",
  treatment = NA,
  dbdir = getwd(),
  mincov = 10
)

Arguments

Value

returns methylRaw, methylRawList, methylRawDB, methylRawListDB object

Details

The output of methRead is determined by specific input arguments,as there are location, sample.id, assembly and dbtype. The first three are obligatory, while if the last argument is given database features are enabled. If then location refers to an uncompressed file the function will create a flat file database and the associated methylRawDB object will link to this database. If then location refers to an earlier created database file then the object will directly link to this database, skipping the preprocessing steps.

When pipeline argument is a list, it is exptected to provide a named list with following names. 'fraction' is a logical value, denoting if the column frequency of Cs has a range from [0-1] or [0-100]. If true it assumes range is [0-1]. 'chr.col" is the number of the column that has chrosome string. 'start.col' is the number of the column that has start coordinate of the base/region of the methylation event. 'end.col' is the number of the column that has end coordinate of the base/region of the methylation event. 'coverage.col' is the number of the column that has read coverage values. 'strand.col' is the number of the column that has strand information, the strand information in the file has to be in the form of '+' or '-', 'freqC.col' is the number of the column that has the frequency of Cs. See examples to see how to read a generic methylation text file.

Other possible values for pipeline argument are 'amp','bismark', 'bismarkCoverage' and 'bismarkCytosineReport'. For 'amp' and 'bismark' the function expects a tabular format shown in the webpage (http://github.com/al2na/methylKit). "amp" and "bismark" expect identical input and are kept for historical reasons. 'amp' was a pipeline used in Akalin et al. 2012 Plos Genetics paper, publicly available in googlecode.

Bismark aligner can output methylation information per base in multiple formats. With pipeline='bismarkCoverage', the function reads bismark coverage files, which have chr,start,end, number of cytosines (methylated bases) and number of thymines (unmethylated bases) format. If bismark coverage files are used the function will not have the strand information,so beware of that fact. With pipeline='bismarkCytosineReport', the function expects cytosine report files from Bismark, which have chr,start, strand, number of cytosines (methylated bases) , number of thymines (unmethylated bases),context and trinucletide context format.

The function can also read gzipped files. On unix systems, this is achieved by using zcat filename and feeding that into data.table::fread . On Windows, the file is first uncompressed then read into R using data.table::fread.

Examples

# this is a list of example files, ships with the package
# for your own analysis you will just need to provide set of paths to files
# you will not need the "system.file(..."  part
file.list=list(
         system.file("extdata", "test1.myCpG.txt", package = "methylKit"),
         system.file("extdata", "test2.myCpG.txt", package = "methylKit"),
         system.file("extdata", "control1.myCpG.txt", package = "methylKit"),
         system.file("extdata", "control2.myCpG.txt", package = "methylKit") 
         )

# read the files to a methylRawList object: myobj
myobj=methRead(file.list,
            sample.id=list("test1","test2","ctrl1","ctrl2"),
            assembly="hg18",treatment=c(1,1,0,0))
            
# read one file as methylRaw object
myobj=methRead( file.list[[1]],
            sample.id="test1",assembly="hg18")
            
# read a generic text file containing CpG methylation values
# let's first look at the content of the file
generic.file=system.file("extdata", "generic1.CpG.txt",package = "methylKit")
read.table(generic.file,header=TRUE)

# And this is how you can read that generic file as a methylKit object            
 myobj=methRead( generic.file,
             pipeline=list(fraction=FALSE,chr.col=1,start.col=2,end.col=2,
                           coverage.col=4,strand.col=3,freqC.col=5),
             sample.id="test1",assembly="hg18")
            
# This creates tabix files that save methylation data
# Without specified dbdir first creates a folder named the following 
# in working directory:
# paste("methylDB",Sys.Date(),paste(sample(c(0:9, letters, LETTERS),3,
# replace=TRUE),collapse=""))
#
# Then, saves tabix files from methylKit objects there
 myobj=methRead( file.list,
               sample.id=list("test1","test2","ctrl1","ctrl2"),
               assembly="hg18",treatment=c(1,1,0,0),
               dbtype="tabix") 

# This creates a single tabix files that saves methylation data
# first creates a "methylDB_objects" directory
# Then, saves tabix file from methylKit objects there
 myobj=methRead(file.list[[1]],
               sample.id="test1",
               assembly="hg18",
               dbtype="tabix",dbdir="methylDB_objects")
           
 # tidy up                  
 rm(myobj)              
 unlink(list.files(pattern = "methylDB",full.names = TRUE),recursive = TRUE)

Description

The function uses a segmentation algorithm (fastseg) to segment the methylation profiles. Following that, it uses gaussian mixture modelling to cluster the segments into k components. This process uses mean methylation value of each segment in the modeling phase. Each component ideally indicates quantitative classification of segments, such as high or low methylated regions.

Usage

methSeg(
  obj,
  diagnostic.plot = TRUE,
  join.neighbours = FALSE,
  initialize.on.subset = 1,
  ...
)

Arguments

Details

To be sure that the algorithm will work on your data, the object should have at least 5000 records

After initial segmentation with fastseg(), segments are clustered into self-similar groups based on their mean methylation profile using mixture modeling. Mixture modeling estimates the initial parameters of the distributions by using the whole dataset. If "initialize.on.subset" argument set as described, the function will use a subset of the data to initialize the parameters of the mixture modeling prior to the Expectation-maximization (EM) algorithm used by Mclust package.

Value

A GRanges object with segment classification and information. 'seg.mean' column shows the mean methylation per segment. 'seg.group' column shows the segment groups obtained by mixture modeling

Author(s)

Altuna Akalin, contributions by Arsene Wabo and Katarzyna Wreczycka

See Also

methSeg2bed, joinSegmentNeighbours

Examples

download.file(
 "https://raw.githubusercontent.com/BIMSBbioinfo/compgen2018/master/day3_diffMeth/data/H1.chr21.chr22.rds",
 destfile="H1.chr21.chr22.rds",method="curl")

 mbw=readRDS("H1.chr21.chr22.rds")

 # it finds the optimal number of componets as 6
 res=methSeg(mbw,diagnostic.plot=TRUE,maxInt=100,minSeg=10)

 # however the BIC stabilizes after 4, we can also try 4 componets
 res=methSeg(mbw,diagnostic.plot=TRUE,maxInt=100,minSeg=10,G=1:4)

 # get segments to BED file
 methSeg2bed(res,filename="H1.chr21.chr22.trial.seg.bed")


unlink(list.files(pattern="H1.chr21.chr22",full.names=TRUE))

Description

The segments are color coded based on their score (methylation or differential methylation value). They are named by segment group (components in mixture modeling) and the score in the BED file is obtained from 'seg.mean' column of segments object.

Usage

methSeg2bed(
  segments,
  filename,
  trackLine = "track name='meth segments' description='meth segments' itemRgb=On",
  colramp = colorRamp(c("gray", "green", "darkgreen"))
)

Arguments

Value

A BED files with the segmented data which can be visualized in the UCSC browser

See Also

methSeg

Description

This class is designed to contain methylation information such as coverage, number of methylated bases, etc.. The methylation events contained in the class must be sampled in multiple experiments (ex: only CpG bases covered in multiple experiments are stored in the object of this class). The class extends data.frame and creates an object that holds methylation information and genomic location. The object belonging to this class is produced by unite function.

Slots

sample.ids:

character vector for ids of samples in the object

assembly:

name of the genome assembly

context:

context of methylation. Ex: CpG,CpH,CHH, etc

treatment:

treatment vector denoting which samples are test and control

coverage.index:

vector denoting which columns in the data correspons to coverage values

numCs.index:

vector denoting which columns in the data correspons to number of methylatedCs values

numTs.index:

vector denoting which columns in the data correspons to number of unmethylated Cs values

destranded:

logical value. If TRUE object is destranded, if FALSE it is not.

resolution:

resolution of methylation information, allowed values: 'base' or 'region'

Details

methylBase class extends data.frame class therefore providing novice and experienced R users with a data structure that is well known and ubiquitous in many R packages.

Subsetting

In the following code snippets, x is a methylBase. Subsetting by x[i,] will produce a new object if subsetting is done on rows. Column subsetting is not directly allowed to prevent errors in the downstream analysis. see ?methylKit[ .

Accessors

The following functions provides access to data slots of methylDiffDB: - getData: get the data slot from the methylKit objects, - getAssembly: get assembly of the genome, - getContext: get the context of methylation

Coercion

methylBase object can be coerced to GRanges object via as function.

Examples

data(methylKit)
library(GenomicRanges)
my.gr=as(methylBase.obj,"GRanges")

Description

methylBase, methylDiff and methylRawList. You can load the data using data(methylKit)

Format

methylBase.obj object stores the location and methylation information for bases that are covered in all samples. methylBase partially extends data.frame S3 class.

Description

This class is designed to contain methylation information such as coverage, number of methylated bases, etc... The class creates an object that holds methylation information and genomic location as flat file database. The object belonging to this class is produced by unite function.

Slots

dbpath:

path to flat file database(s)

num.records:

number of records (lines) in the object

sample.ids:

character vector for ids of samples in the object

assembly:

name of the genome assembly

context:

context of methylation. Ex: CpG,CpH,CHH, etc

treatment:

treatment vector denoting which samples are test and control

coverage.index:

vector denoting which columns in the data correspond to coverage values

numCs.index:

vector denoting which columns in the data correspond to number of methylatedCs values

numTs.index:

vector denoting which columns in the data correspond to number of unmethylated Cs values

destranded:

logical value. If TRUE object is destranded, if FALSE it is not.

resolution:

resolution of methylation information, allowed values: 'base' or 'region'

dbtype:

string for type of the flat file database, ex: tabix

Details

methylBaseDB class has the same functionality as methylBase class, but the data is saved in a flat database file and therefore allocates less space in memory.

Subsetting

In the following code snippets, x is a methylBaseDB. Subsetting by x[i,] will produce a new methylBase object if subsetting is done on rows. Column subsetting is not directly allowed to prevent errors in the downstream analysis. see ?methylKit[ .

Accessors

The following functions provides access to data slots of methylDiffDB: - getData: get the data slot from the methylKit objects, - getAssembly: get assembly of the genome, - getContext: get the context of methylation

Coercion

methylBaseDB object can be coerced to: GRanges object via as function. methylBase object via as function.

Examples

data(methylKit)
methylBaseDB.obj <- unite(methylRawList.obj,save.db=TRUE,dbdir="methylDB")
library(GenomicRanges)
my.gr=as(methylBaseDB.obj,"GRanges")


# remove Database again
rm(methylBaseDB.obj)
unlink("methylDB",recursive=TRUE)

Description

This class is designed to hold statistics and locations for differentially methylated regions/bases. It extends data.frame class. calculateDiffMeth function returns an object with methylDiff class.

Slots

sample.ids

ids/names of samples in a vector

assembly

a name of genome assembly, such as :hg18,mm9, etc

context

numeric vector identifying which samples are which group

treatment

numeric vector identifying which samples are which group

destranded

logical denoting if methylation inormation is destranded or not

resolution

string either 'base' or 'region' defining the resolution of methylation information

.Data

data.frame holding the locations and statistics

Details

methylDiff class extends data.frame class therefore providing novice and experienced R users with a data structure that is well known and ubiquitous in many R packages.

Subsetting

In the following code snippets, x is a methylDiff object. Subsetting by x[i,] will produce a new object if subsetting is done on rows. Column subsetting is not directly allowed to prevent errors in the downstream analysis. see ?methylKit[ .

Coercion

methylDiff object can be coerced to GRanges object via as function.

Accessors

The following functions provides access to data slots of methylDiffDB: - getData: get the data slot from the methylKit objects, - getAssembly: get assembly of the genome, - getContext: get the context of methylation

Examples

data(methylKit)
library(GenomicRanges)
my.gr=as(methylDiff.obj,"GRanges")

Description

methylBase, methylDiff and methylRawList. You can load the data using data(methylKit)

Format

The Differential methylation information is stored in methylDiff.obj object. methylBase partially extends data.frame S3 class.

Description

This class is designed to hold statistics and locations for differentially methylated regions/bases as flat file database. calculateDiffMeth function returns an object with methylDiffDB class.

Slots

dbpath:

path to flat file database(s)

num.records:

number of records (lines) in the object

sample.ids

ids/names of samples in a vector

assembly

a name of genome assembly, such as :hg18,mm9, etc

context

numeric vector identifying which samples are which group

treatment

numeric vector identifying which samples are which group

destranded

logical denoting if methylation inormation is destranded or not

resolution

string either 'base' or 'region' defining the resolution of methylation information

dbtype:

string for type of the flat file database, ex: tabix

Details

methylDiffDB class has the same functionality as methylDiff class, but the data is saved in a flat database file and therefore allocates less space in memory.

Subsetting

In the following code snippets, x is a methylDiffDB. Subsetting by x[i,] will produce a new object if subsetting is done on rows. Column subsetting is not directly allowed to prevent errors in the downstream analysis. see ?methylKit[ .

Coercion

methylDiffDB object can be coerced to: GRanges object via as function. methylDiff object via as function.

Accessors

The following functions provides access to data slots of methylDiffDB: - getData: get the data slot from the methylKit objects, - getAssembly: get assembly of the genome, - getContext: get the context of methylation

Examples

data(methylKit)

methylDiffDB.obj <- calculateDiffMeth(methylBase.obj,save.db=TRUE,dbdir="methylDB")

library(GenomicRanges)
my.gr=as(methylDiffDB.obj,"GRanges")

# remove Database again
rm(methylDiffDB.obj)
unlink("methylDB",recursive=TRUE)

Description

These are deprecated or defunct functions. Most of them are replaced by genomation functions. See the vignette for examples on how to use genomation functions for annotation purposes.

Usage

annotate.WithFeature()

annotate.WithFeature.Flank()

annotate.WithGenicParts()

read.bed()

read.feature.flank()

read.transcript.features()

getFeatsWithTargetsStats()

getFlanks()

getMembers()

getTargetAnnotationStats()

plotTargetAnnotation()

read()

read.bismark()

adjust.methylC()

get.methylDiff()

Description

This object stores the raw mehylation data that is read in through read function and extends data.frame.The raw methylation data is basically percent methylation values and read coverage values per genomic base/region.

Slots

sample.id:

string for an identifier of the sample

assembly:

string for genome assembly, ex: hg18,hg19,mm9

context:

methylation context string, ex: CpG,CpH,CHH, etc.

resolution:

resolution of methylation information, 'base' or 'region'

Details

methylRaw class extends data.frame class therefore providing novice and experienced R users with a data structure that is well known and ubiquitous in many R packages.

Subsetting

In the following code snippets, x is a methylRaw. Subsetting by x[i,] will produce a new object if subsetting is done on rows. Column subsetting is not directly allowed to prevent errors in the downstream analysis. see ?methylKit[ .

Accessors

The following functions provides access to data slots of methylDiffDB: - getData: get the data slot from the methylKit objects, - getAssembly: get assembly of the genome, - getContext: get the context of methylation

Coercion

methylRaw object can be coerced to GRanges object via as function.

Examples

# example of a raw methylation data as a text file
read.table(system.file("extdata", "control1.myCpG.txt", 
                                package = "methylKit"),
                                    header=TRUE,nrows=5)

data(methylKit)

# example of a methylRaw object
head(methylRawList.obj[[1]])
str(head(methylRawList.obj[[1]]))

library(GenomicRanges)

#coercing methylRaw object to GRanges object
my.gr=as(methylRawList.obj[[1]],"GRanges")

Description

This object stores the raw mehylation data that is read in through read function as flat file database.The raw methylation data is basically percent methylation values and read coverage values per genomic base/region.

Slots

dbpath:

path to flat file database

num.records:

number of records (lines) in the object

sample.id:

string for an identifier of the sample

assembly:

string for genome assembly, ex: hg18,hg19,mm9

context:

methylation context string, ex: CpG,CpH,CHH, etc.

resolution:

resolution of methylation information, 'base' or 'region'

dbtype:

string for type of the flat file database, ex: tabix

Details

methylRawDB is created via read function and has the same functionality as methylRaw class, but the data is saved in a flat database file and therefore allocates less space in memory.

Subsetting

In the following code snippets, x is a methylRawDB. Subsetting by x[i,] will produce a new methylRaw object if subsetting is done on rows. Column subsetting is not directly allowed to prevent errors in the downstream analysis. see ?methylKit[ . x[] will return the methylRawDB object as new methylRaw object

Accessors

The following functions provides access to data slots of methylDiffDB: - getData: get the data slot from the methylKit objects, - getAssembly: get assembly of the genome, - getContext: get the context of methylation

Coercion

methylRawDB object can be coerced to: GRanges object via as function. methylRaw object via as function.

Examples

# example of a raw methylation data contained as a text file
read.table(system.file("extdata", "control1.myCpG.txt", package = "methylKit"),
header=TRUE,nrows=5)


methylRawDB.obj <- methRead(
system.file("extdata", "control1.myCpG.txt", package = "methylKit"),
                        sample.id = "ctrl1", assembly = "hg18",
                        dbtype = "tabix", dbdir = "methylDB")

# example of a methylRawDB object
methylRawDB.obj
str(methylRawDB.obj)

library(GenomicRanges)

#coercing methylRawDB object to GRanges object
my.gr=as(methylRawDB.obj,"GRanges")

#coercing methylRawDB object to methylRaw object
myRaw=as(methylRawDB.obj,"methylRaw")

# remove Database again
rm(methylRawDB.obj)
unlink("methylDB",recursive=TRUE)

Description

This class stores the list of methylRaw objects. Functions such as lapply can be used on this list. It extends list class. This object is primarily produced by methRead function.

Usage

methylRawList(..., treatment)

Arguments

Slots

treatment

numeric vector denoting control and test samples

.Data

a list of methylRaw objects

Constructor

methylRawList(...)

combine multiple methylRaw objects supplied in ... into a methylRawList object.

Examples

data(methylKit)

#applying functions designed for methylRaw on methylRawList object
lapply(methylRawList.obj,"getAssembly")

Description

methylBase, methylDiff and methylRawList. You can load the data using data(methylKit)

Format

Methylation data from multiple the samples regardless of common coverage are stored in methylRawList.obj object. methylRawList extends list S3 class

Description

This class stores the list of methylRawDB objects. Functions such as lapply can be used on this list. It extends list class. This object is primarily produced by methRead function.

Usage

methylRawListDB(..., treatment)

Arguments

Slots

treatment

numeric vector denoting control and test samples

.Data

a list of methylRawDB objects

Constructor

methylRawListDB(...)

combine multiple methylRawDB objects supplied in ... into a methylRawListDB object.

Examples

file.list=list( system.file("extdata", "test1.myCpG.txt", 
package = "methylKit"),
                system.file("extdata", "test2.myCpG.txt", 
                package = "methylKit"),
                system.file("extdata", "control1.myCpG.txt", 
                package = "methylKit"),
                system.file("extdata", "control2.myCpG.txt", 
                package = "methylKit") )

methylRawListDB.obj <- methRead(file.list,
sample.id = list("test1","test2","ctrl1","ctrl2"),
                            assembly = "hg18",treatment = c(1,1,0,0),
                            dbtype = "tabix",dbdir = "methylDB")

#applying functions designed for methylRawDB on methylRawListDB object
lapply(methylRawListDB.obj,"getAssembly")


# remove Database again
rm(methylRawListDB.obj)
unlink("methylDB",recursive=TRUE)

Description

The function normalizes coverage values between samples using a scaling factor derived from differences between mean or median of coverage distributions

Usage

normalizeCoverage(obj,method="median",chunk.size,save.db,...)

## S4 method for signature 'methylRawList'
normalizeCoverage(
  obj,
  method = "median",
  chunk.size = 1e+06,
  save.db = FALSE,
  ...
)

## S4 method for signature 'methylRawListDB'
normalizeCoverage(
  obj,
  method = "median",
  chunk.size = 1e+06,
  save.db = TRUE,
  ...
)

Arguments

Value

a methylRawList or methylRawList object depending on class of input object

Details

The parameter chunk.size is only used when working with methylRawListDB objects, as they are read in chunk by chunk to enable processing large-sized objects which are stored as flat file database. Per default the chunk.size is set to 1M rows, which should work for most systems. If you encounter memory problems or have a high amount of memory available feel free to adjust the chunk.size.

The parameter save.db is per default TRUE for methylDB objects as methylRawListDB, while being per default FALSE for methylRawList. If you wish to save the result of an in-memory-calculation as flat file database or if the size of the database allows the calculation in-memory, then you might want to change the value of this parameter.

Author(s)

Altuna Akalin

Examples

data(methylKit)
# normalize by the median coverage
newObj = normalizeCoverage(methylRawList.obj,method="median")

# normalize by mean coverage and save to database in folder methylDB
newDBObj = normalizeCoverage(methylRawList.obj,method="mean",
save.db=TRUE,dbdir="methylDB")

Description

The function does a PCA analysis using prcomp function using percent methylation matrix as an input.

Usage

PCASamples(.Object, screeplot=FALSE, adj.lim=c(0.0004,0.1), scale=TRUE,
center=TRUE,comp=c(1,2),transpose=TRUE,sd.filter=TRUE,
           sd.threshold=0.5,filterByQuantile=TRUE,obj.return=FALSE,chunk.size)

## S4 method for signature 'methylBase'
PCASamples(
  .Object,
  screeplot,
  adj.lim,
  scale,
  center,
  comp,
  transpose,
  sd.filter,
  sd.threshold,
  filterByQuantile,
  obj.return
)

## S4 method for signature 'methylBaseDB'
PCASamples(
  .Object,
  screeplot = FALSE,
  adj.lim = c(4e-04, 0.1),
  scale = TRUE,
  center = TRUE,
  comp = c(1, 2),
  transpose = TRUE,
  sd.filter = TRUE,
  sd.threshold = 0.5,
  filterByQuantile = TRUE,
  obj.return = FALSE,
  chunk.size = 1e+06
)

Arguments

Value

The form of the value returned by PCASamples is the summary of principal component analysis by prcomp.

Details

The parameter chunk.size is only used when working with methylBaseDB objects, as they are read in chunk by chunk to enable processing large-sized objects which are stored as flat file database. Per default the chunk.size is set to 1M rows, which should work for most systems. If you encounter memory problems or have a high amount of memory available feel free to adjust the chunk.size.

Note

cor option is not in use anymore, since prcomp is used for PCA analysis instead of princomp

Examples

data(methylKit) 

# do PCA with filtering rows with low variation, filter rows with standard 
# deviation lower than the 50th percentile of Standard deviation distribution
PCASamples(methylBase.obj,screeplot=FALSE, adj.lim=c(0.0004,0.1),
           scale=TRUE,center=TRUE,comp=c(1,2),transpose=TRUE,sd.filter=TRUE,
           sd.threshold=0.5,filterByQuantile=TRUE,obj.return=FALSE)

Description

get percent methylation scores from methylBase or methylBaseDB object

Usage

percMethylation(
  methylBase.obj,
  rowids = FALSE,
  save.txt = FALSE,
  chunk.size = 1e+06
)

## S4 method for signature 'methylBase'
percMethylation(methylBase.obj, rowids = FALSE)

## S4 method for signature 'methylBaseDB'
percMethylation(
  methylBase.obj,
  rowids = FALSE,
  save.txt = FALSE,
  chunk.size = 1e+06
)

Arguments

Value

matrix with percent methylation values per base/region across all samples, row names would be base/region identifiers

Details

The parameter chunk.size is only used when working with methylBaseDB objects, as they are read in chunk by chunk to enable processing large-sized objects which are stored as flat file database. Per default the chunk.size is set to 1M rows, which should work for most systems. If you encounter memory problems or have a high amount of memory available feel free to adjust the chunk.size.

Examples

data(methylKit)
mat=percMethylation(methylBase.obj)
head(mat)

Description

The function sums up coverage, numCs and numTs values within each group so one representative sample for each group will be created in a new methylBase object

Usage

pool(obj, sample.ids, chunk.size = 1e+06, save.db = FALSE, ...)

## S4 method for signature 'methylBase'
pool(obj, sample.ids, chunk.size = 1e+06, save.db = FALSE, ...)

## S4 method for signature 'methylBaseDB'
pool(obj, sample.ids, chunk.size = 1e+06, save.db = TRUE, ...)

Arguments

Value

a methylBase or methylBaseDB object depending on class of input object

Details

The parameter chunk.size is only used when working with methylBaseDB objects, as they are read in chunk by chunk to enable processing large-sized objects which are stored as flat file database. Per default the chunk.size is set to 1M rows, which should work for most systems. If you encounter memory problems or have a high amount of memory available feel free to adjust the chunk.size.

The parameter save.db is per default TRUE for methylDB objects as methylBaseDB, while being per default FALSE for methylBase. If you wish to save the result of an in-memory-calculation as flat file database or if the size of the database allows the calculation in-memory, then you might want to change the value of this parameter.

Author(s)

Altuna Akalin

Examples

data(methylKit)

# methylBase.obj has two groups, each group has two samples,
# the following function will pool the samples in each group
# so that each group will be represented by one pooled sample
pooled.methylBase=pool(methylBase.obj,sample.ids=c("test","control"))

Description

The function calls methylation percentage per base from sorted Bismark SAM or BAM files and reads methylation information as methylKit objects. Bismark is a popular aligner for high-throughput bisulfite sequencing experiments and it outputs its results in SAM format by default, and can be converted to BAM. Bismark SAM/BAM format contains aligner specific tags which are absolutely necessary for methylation percentage calling using processBismarkAln. SAM/BAM files from other aligners will not work with this function.

Usage

processBismarkAln(
  location,
  sample.id,
  assembly,
  save.folder = NULL,
  save.context = c("CpG"),
  read.context = "CpG",
  nolap = FALSE,
  mincov = 10,
  minqual = 20,
  phred64 = FALSE,
  treatment = NULL,
  save.db = FALSE,
  verbose = 1
)

## S4 method for signature 'character,character,character'
processBismarkAln(
  location,
  sample.id,
  assembly,
  save.folder = NULL,
  save.context = c("CpG"),
  read.context = "CpG",
  nolap = FALSE,
  mincov = 10,
  minqual = 20,
  phred64 = FALSE,
  treatment = NULL,
  save.db = FALSE,
  verbose = 1
)

## S4 method for signature 'list,list,character'
processBismarkAln(
  location,
  sample.id,
  assembly,
  save.folder = NULL,
  save.context = c("CpG"),
  read.context = "CpG",
  nolap = FALSE,
  mincov = 10,
  minqual = 20,
  phred64 = FALSE,
  treatment = NULL,
  save.db = FALSE,
  verbose = 1
)

Arguments

Value

methylRaw or methylRawList object

Note

SAM files should be sorted with samtools sort or unix sort. Other sorting methods can alter the order of fields(columns) in the SAM file and that will result in an error when using processBismarkAln().

Examples

# reading one bismark file:
my.file=system.file("extdata", "test.fastq_bismark.sorted.min.sam", 
                              package = "methylKit")
obj=processBismarkAln(my.file,"test",assembly="hg18",save.folder=NULL,
                 save.context="CpG",read.context="CpG")
 
# reading multiple files
file.list2=list(system.file("extdata", "test.fastq_bismark.sorted.min.sam", 
package = "methylKit"),
               system.file("extdata", "test.fastq_bismark.sorted.min.sam", 
               package = "methylKit"),
              system.file("extdata", "test.fastq_bismark.sorted.min.sam", 
              package = "methylKit"),
               system.file("extdata", "test.fastq_bismark.sorted.min.sam",
                package = "methylKit"))

 objs=processBismarkAln(location=file.list2
             ,sample.id=list("test1","test2","ctrl1","ctrl1"),assembly="hg18",
             save.folder=NULL,save.context=NULL,read.context="CpG",
             nolap=FALSE,mincov=10,minqual=20,phred64=FALSE,
             treatment=c(1,1,0,0))

Description

The function reads the header from a given tabix file and loads it into corresponding methylDB object.

Usage

readMethylDB(dbpath)

Arguments

Value

an methylBaseDB,methylRawDB, methylRawListDB or an methylDiffDB object

Examples

## Not run: 
data(methylKit)

baseDB.obj <- makeMethylDB(methylBase.obj,"my/path")
mydbpath <- getDBPath(baseDB.obj)
rm(baseDB.obj)
readMethylDB(mydbpath)


## End(Not run)

Description

The function reconstructs a new methylBase object from an input methylBase object and percent methylation matrix. Basically, it uses the read coverages in the input methylBase object and deduces new number of methylated Cs and unmethylated Cs based on the input percent methylation matrix. It is ideally to be used to reconstruct methylBase objects after batch correction on percent methylation values. The percent methylation matrix rows must match methylBase object rows in order ,and in addition column order (the order of samples) in input methylBase must match the order in percent methylation matrix.

Usage

reconstruct(methMat, mBase, chunk.size = 1e+06, save.db = FALSE, ...)

## S4 method for signature 'ANY,methylBase'
reconstruct(methMat, mBase, chunk.size = 1e+06, save.db = FALSE, ...)

## S4 method for signature 'ANY,methylBaseDB'
reconstruct(methMat, mBase, chunk.size = 1e+06, save.db = TRUE, ...)

Arguments

Value

new methylBase or methylBase object where methylation percentage matches input methMat and coverages matches input mBase

Details

The parameter chunk.size is only used when working with methylBaseDB objects, as they are read in chunk by chunk to enable processing large-sized objects which are stored as flat file database. Per default the chunk.size is set to 1M rows, which should work for most systems. If you encounter memory problems or have a high amount of memory available feel free to adjust the chunk.size.

The parameter save.db is per default TRUE for methylDB objects as methylBaseDB, while being per default FALSE for methylBase. If you wish to save the result of an in-memory-calculation as flat file database or if the size of the database allows the calculation in-memory, then you might want to change the value of this parameter.

Note

Batch effect correction (if any batch effect exists) is a tricky issue. We provide some simple ways to deal with it (see assocComp and removeComp ), But if you can find other ways to correct for batch effects and want to create a methylBase object with the corrected percent methylation values, you can use this function.

Author(s)

Altuna Akalin

Examples

data(methylKit)

# get percent methylation
mat=percMethylation(methylBase.obj)

# do some changes in the matrix
# this is just a toy example
# ideally you want to correct the matrix
# for batch effects
mat[mat==100]=80

# reconstruct the methylBase from the corrected matrix
newobj=reconstruct(mat,methylBase.obj)

Description

Convert methylRaw, methylRawDB, methylRawList, methylRawListDB, methylBase or methylBaseDB object into regional counts for a given GRanges or GRangesList object.

Usage

regionCounts(object,regions,cov.bases=0,strand.aware=FALSE,chunk.size,save.db,...)

## S4 method for signature 'methylRaw,GRanges'
regionCounts(
  object,
  regions,
  cov.bases = 0,
  strand.aware = FALSE,
  chunk.size = 1e+06,
  save.db = FALSE,
  ...
)

## S4 method for signature 'methylBase,GRanges'
regionCounts(
  object,
  regions,
  cov.bases = 0,
  strand.aware = FALSE,
  chunk.size = 1e+06,
  save.db = FALSE,
  ...
)

## S4 method for signature 'methylRaw,GRangesList'
regionCounts(
  object,
  regions,
  cov.bases = 0,
  strand.aware = FALSE,
  chunk.size = 1e+06,
  save.db = FALSE,
  ...
)

## S4 method for signature 'methylBase,GRangesList'
regionCounts(
  object,
  regions,
  cov.bases = 0,
  strand.aware = FALSE,
  chunk.size = 1e+06,
  save.db = FALSE,
  ...
)

## S4 method for signature 'methylRawList,GRanges'
regionCounts(
  object,
  regions,
  cov.bases = 0,
  strand.aware = FALSE,
  chunk.size = 1e+06,
  save.db = FALSE,
  ...
)

## S4 method for signature 'methylRawList,GRangesList'
regionCounts(
  object,
  regions,
  cov.bases = 0,
  strand.aware = FALSE,
  chunk.size = 1e+06,
  save.db = FALSE,
  ...
)

## S4 method for signature 'methylRawDB,GRanges'
regionCounts(
  object,
  regions,
  cov.bases = 0,
  strand.aware = FALSE,
  chunk.size = 1e+06,
  save.db = TRUE,
  ...
)

## S4 method for signature 'methylRawDB,GRangesList'
regionCounts(
  object,
  regions,
  cov.bases = 0,
  strand.aware = FALSE,
  chunk.size = 1e+06,
  save.db = TRUE,
  ...
)

## S4 method for signature 'methylRawListDB,GRanges'
regionCounts(
  object,
  regions,
  cov.bases = 0,
  strand.aware = FALSE,
  chunk.size = 1e+06,
  save.db = TRUE,
  ...
)

## S4 method for signature 'methylRawListDB,GRangesList'
regionCounts(
  object,
  regions,
  cov.bases = 0,
  strand.aware = FALSE,
  chunk.size = 1e+06,
  save.db = TRUE,
  ...
)

## S4 method for signature 'methylBaseDB,GRanges'
regionCounts(
  object,
  regions,
  cov.bases = 0,
  strand.aware = FALSE,
  chunk.size = 1e+06,
  save.db = TRUE,
  ...
)

## S4 method for signature 'methylBaseDB,GRangesList'
regionCounts(
  object,
  regions,
  cov.bases = 0,
  strand.aware = FALSE,
  chunk.size = 1e+06,
  save.db = TRUE,
  ...
)

Arguments

Value

a new methylRaw,methylBase or methylRawList object. If strand.aware is set to FALSE (default). Even though the resulting object will have the strand information of regions it will still contain methylation information from both strands.

Details

The given regions (Granges/GrangesList object) will be orderd based on chromosome and position before searching for overlaps, so the resulting methylKit object might have a different ording than expected. We are doing this is to ensure that resulting output is consistent for in-memory and database based objects, as database based objects always have to be sorted to enable tabix indexing and providing fast random access.

If you to still want get a custom ordering of the output regions you can access the single regions in any object providing your indices to the select or extract functions.

The parameter chunk.size is only used when working with methylRawDB, methylBaseDB or methylRawListDB objects, as they are read in chunk by chunk to enable processing large-sized objects which are stored as flat file database. Per default the chunk.size is set to 1M rows, which should work for most systems. If you encounter memory problems or have a high amount of memory available feel free to adjust the chunk.size.

The parameter save.db is per default TRUE for methylDB objects as methylRawDB, methylBaseDB or methylRawListDB, while being per default FALSE for methylRaw, methylBase or methylRawList. If you wish to save the result of an in-memory-calculation as flat file database or if the size of the database allows the calculation in-memory, then you might want to change the value of this parameter.

Examples

data(methylKit)

# get the windows of interest as a GRanges object, this can be any set 
# of genomic locations
library(GenomicRanges)
my.win=GRanges(seqnames="chr21",
ranges=IRanges(start=seq(from=9764513,by=10000,length.out=20),width=5000) )

# getting counts per region
regional.methylRaw=regionCounts(object=methylRawList.obj, regions=my.win, 
cov.bases=0,strand.aware=FALSE)

Description

This function can remove a given principal componet from a given methylBase object. First, it calculates principal components from percent methylation matrix and removes the given component(s), reconstructs the methylation matrix then reconstructs number of methylated and unmethylated Cs per position based on the reconstructed percent methylation matrix, and finally returns a new methylBase object.

Usage

removeComp(mBase, comp = NULL, chunk.size = 1e+06, save.db = FALSE, ...)

## S4 method for signature 'methylBase'
removeComp(mBase, comp = NULL, chunk.size = 1e+06, save.db = FALSE, ...)

## S4 method for signature 'methylBaseDB'
removeComp(mBase, comp = NULL, chunk.size = 1e+06, save.db = TRUE, ...)

Arguments

Value

new methylBase or methylBaseDB object

Details

The parameter chunk.size is only used when working with methylBaseDB objects, as they are read in chunk by chunk to enable processing large-sized objects which are stored as flat file database. Per default the chunk.size is set to 1M rows, which should work for most systems. If you encounter memory problems or have a high amount of memory available feel free to adjust the chunk.size.

The parameter save.db is per default TRUE for methylDB objects as methylBaseDB, while being per default FALSE for methylBase. If you wish to save the result of an in-memory-calculation as flat file database or if the size of the database allows the calculation in-memory, then you might want to change the value of this parameter.

Examples

data(methylKit)

# remove 1st principal component
newObj=removeComp(methylBase.obj,comp=1)

# remove 3rd and 4th  principal components
newObj=removeComp(methylBase.obj,comp=c(3,4))

Description

The function creates a new methylRawList, methylRawListDB, methylBase or methylBaseDB object by selecting a subset of samples from the input object, which is a methylRawList or methylBase object. You can use the function to partition a large methylRawList or methylBase object to smaller object based on sample ids or when you want to reorder samples and/or give a new treatmet vector.

Usage

reorganize(
  methylObj,
  sample.ids,
  treatment,
  chunk.size = 1e+06,
  save.db = FALSE,
  ...
)

## S4 method for signature 'methylBase'
reorganize(
  methylObj,
  sample.ids,
  treatment,
  chunk.size = 1e+06,
  save.db = FALSE,
  ...
)

## S4 method for signature 'methylRawList'
reorganize(
  methylObj,
  sample.ids,
  treatment,
  chunk.size = 1e+06,
  save.db = FALSE,
  ...
)

## S4 method for signature 'methylRawListDB'
reorganize(
  methylObj,
  sample.ids,
  treatment,
  chunk.size = 1e+06,
  save.db = TRUE,
  ...
)

## S4 method for signature 'methylBaseDB'
reorganize(
  methylObj,
  sample.ids,
  treatment,
  chunk.size = 1e+06,
  save.db = TRUE,
  ...
)

Arguments

Value

returns a methylRawList, methylRawListDB, methylBase or methylBaseDB object depending on the input object

Details

The parameter chunk.size is only used when working with methylBaseDB or methylRawListDB objects, as they are read in chunk by chunk to enable processing large-sized objects which are stored as flat file database. Per default the chunk.size is set to 1M rows, which should work for most systems. If you encounter memory problems or have a high amount of memory available feel free to adjust the chunk.size.

The parameter save.db is per default TRUE for methylDB objects as methylBaseDB and methylRawListDB, while being per default FALSE for methylBase and methylRawList. If you wish to save the result of an in-memory-calculation as flat file database or if the size of the database allows the calculation in-memory, then you might want to change the value of this parameter.

Examples

# this is a list of example files, ships with the package
file.list=list( system.file("extdata", "test1.myCpG.txt", package = "methylKit"),
               system.file("extdata", "test2.myCpG.txt", package = "methylKit"),
               system.file("extdata", "control1.myCpG.txt", package = "methylKit"),
               system.file("extdata", "control2.myCpG.txt", package = "methylKit") )


# read the files to a methylRawList object: myobj
myobj=methRead( file.list,
          sample.id=list("test1","test2","ctrl1","ctrl2"),
          assembly="hg18",pipeline="amp",treatment=c(1,1,0,0))
meth=unite(myobj,destrand=TRUE)

# get samples named "test1" and "ctrl2" from myobj and create a new methylRawList object
myobj2=reorganize(myobj,sample.ids=c("test1","ctrl2"),treatment=c(1,0) )

# # get samples named "test1" and "ctrl2" from meth and create a new methylBase object
meth2 =reorganize(meth,sample.ids=c("test1","ctrl2"),treatment=c(1,0) )

Description

The function returns a subset of data contained in the methylKit objects.

Usage

select(x,i)

## S4 method for signature 'methylBase'
select(x, i)

## S4 method for signature 'methylRaw'
select(x, i)

## S4 method for signature 'methylDiff'
select(x, i)

## S4 method for signature 'methylRawDB'
select(x, i)

## S4 method for signature 'methylBaseDB'
select(x, i)

## S4 method for signature 'methylDiffDB'
select(x, i)

Arguments

Value

a methylBase,methylRaw or methylDiff object depending on the input object.

Examples

data(methylKit)


methylRawDB.obj=methRead( system.file("extdata","test1.txt.bgz",package="methylKit"),
                          sample.id="test1", assembly="hg18",
                          dbtype = "tabix",dbdir = "methylDB")

methylBaseDB.obj=unite(methylRawList.obj,save.db=TRUE,dbdir="methylDB")


 # selects first hundred rows, returns a methylRaw object
subset1=select(methylRawList.obj[[1]],1:100)
subset1=select(methylRawDB.obj,1:100)

# selects first hundred rows, returns a methylBase object
subset2=select(methylBase.obj,1:100) 
subset2=select(methylBaseDB.obj,1:100)

# selects first hundred rows, returns a methylDiff object
subset3=select(methylDiff.obj,1:100)




# remove Database again
rm(methylBaseDB.obj)
rm(methylRawDB.obj)
unlink("methylDB",recursive=TRUE)

Description

The function selects records from any methylKit object that lie inside the regions given by ranges of class GRanges and returns an in-memory equivalent of this object

Usage

selectByOverlap(object,ranges)

## S4 method for signature 'methylRaw,GRanges'
selectByOverlap(object, ranges)

## S4 method for signature 'methylRawList,GRanges'
selectByOverlap(object, ranges)

## S4 method for signature 'methylBase,GRanges'
selectByOverlap(object, ranges)

## S4 method for signature 'methylDiff,GRanges'
selectByOverlap(object, ranges)

## S4 method for signature 'methylRawDB,GRanges'
selectByOverlap(object, ranges)

## S4 method for signature 'methylRawListDB,GRanges'
selectByOverlap(object, ranges)

## S4 method for signature 'methylBaseDB,GRanges'
selectByOverlap(object, ranges)

## S4 method for signature 'methylDiffDB,GRanges'
selectByOverlap(object, ranges)

Arguments

Value

a methylBase,methylRaw, methylRawList or methylDiff object depending on the input object.

Author(s)

Alexander Gosdschan

Examples

data(methylKit)

file.list=list( system.file("extdata", "test1.myCpG.txt", package = "methylKit"),
                system.file("extdata", "test2.myCpG.txt", package = "methylKit"),
                system.file("extdata", "control1.myCpG.txt", package = "methylKit"),
                system.file("extdata", "control2.myCpG.txt", package = "methylKit") )

methylRawListDB.obj=methRead(file.list,
                         sample.id=list("test1","test2","ctrl1","ctrl2"),
                         assembly="hg18",treatment=c(1,1,0,0),
                         dbtype = "tabix",dbdir = "methylDB")

methylBaseDB.obj=unite(methylRawListDB.obj)

methylDiffDB.obj = calculateDiffMeth(methylBaseDB.obj)

# define the windows of interest as a GRanges object, this can be any set 
# of genomic locations
library(GenomicRanges)
my.win=GRanges(seqnames="chr21",
ranges=IRanges(start=seq(from=9764513,by=10000,length.out=20),width=5000) )

# selects the records that lie inside the regions
myRaw <- selectByOverlap(methylRawListDB.obj[[1]],my.win)

# selects the records that lie inside the regions
myBase <- selectByOverlap(methylBaseDB.obj,my.win)

# selects the records that lie inside the regions
myDiff <- selectByOverlap(methylDiffDB.obj,my.win)

# selects the records that lie inside the regions
myRaw2 <- selectByOverlap(methylRawList.obj[[1]],my.win)

# selects the records that lie inside the regions
myRawList2 <- selectByOverlap(methylRawList.obj,my.win)

# selects the records that lie inside the regions
myBase2 <- selectByOverlap(methylBase.obj,my.win)

# selects the records that lie inside the regions
myDiff2 <- selectByOverlap(methylDiff.obj,my.win)


rm(methylRawListDB.obj)
rm(methylBaseDB.obj)
rm(methylDiffDB.obj)
unlink("methylDB",recursive=TRUE)

Description

The show method works for methylRaw,methylRawDB, methylRawList,methylRawListDB, methylBase,methylBaseDB and methylDiff objects

Usage

## S4 method for signature 'methylBase'
show(object)

## S4 method for signature 'methylRaw'
show(object)

## S4 method for signature 'methylRawList'
show(object)

## S4 method for signature 'methylDiff'
show(object)

## S4 method for signature 'methylRawDB'
show(object)

## S4 method for signature 'methylRawListDB'
show(object)

## S4 method for signature 'methylBaseDB'
show(object)

## S4 method for signature 'methylDiffDB'
show(object)

Arguments

Examples

data(methylKit)
methylDiff.obj
show(methylDiff.obj)

Description

The function summarizes methylated/unmethylated base counts over tilling windows accross genome. This function can be used when differential methylation analysis is preferable to tilling windows instead of base pairs.

Usage

tileMethylCounts(object,win.size=1000,step.size=1000,cov.bases=0,mc.cores=1,save.db,...)

## S4 method for signature 'methylRaw'
tileMethylCounts(
  object,
  win.size = 1000,
  step.size = 1000,
  cov.bases = 0,
  mc.cores = 1,
  save.db = FALSE,
  ...
)

## S4 method for signature 'methylRawList'
tileMethylCounts(
  object,
  win.size = 1000,
  step.size = 1000,
  cov.bases = 0,
  mc.cores = 1,
  save.db = FALSE,
  ...
)

## S4 method for signature 'methylBase'
tileMethylCounts(
  object,
  win.size = 1000,
  step.size = 1000,
  cov.bases = 0,
  mc.cores = 1,
  save.db = FALSE,
  ...
)

## S4 method for signature 'methylRawDB'
tileMethylCounts(
  object,
  win.size = 1000,
  step.size = 1000,
  cov.bases = 0,
  mc.cores = 1,
  save.db = TRUE,
  ...
)

## S4 method for signature 'methylRawListDB'
tileMethylCounts(
  object,
  win.size = 1000,
  step.size = 1000,
  cov.bases = 0,
  mc.cores = 1,
  save.db = TRUE,
  ...
)

## S4 method for signature 'methylBaseDB'
tileMethylCounts(
  object,
  win.size = 1000,
  step.size = 1000,
  cov.bases = 0,
  mc.cores = 1,
  save.db = TRUE,
  ...
)

Arguments

Value

methylRaw,methylBase or methylRawList object

Details

The parameter chunk.size is only used when working with methylRawDB, methylBaseDB or methylRawListDB objects, as they are read in chunk by chunk to enable processing large-sized objects which are stored as flat file database. Per default the chunk.size is set to 1M rows, which should work for most systems. If you encounter memory problems or have a high amount of memory available feel free to adjust the chunk.size.

The parameter save.db is per default TRUE for methylDB objects as methylRawDB, methylBaseDB or methylRawListDB, while being per default FALSE for methylRaw, methylBase or methylRawList. If you wish to save the result of an in-memory-calculation as flat file database or if the size of the database allows the calculation in-memory, then you might want to change the value of this parameter.

Examples

data(methylKit)

tiled.methylRaw=tileMethylCounts(object=methylRawList.obj,win.size=1000,
                                 step.size=1000,cov.bases=0)

Description

This functions unites methylRawList and methylRawListDB objects that only bases with coverage from all samples are retained. The resulting object is either of class methylBase or methylBaseDB depending on input.

Usage

unite(
  object,
  destrand = FALSE,
  min.per.group = NULL,
  chunk.size = 1e+06,
  mc.cores = 1,
  save.db = FALSE,
  ...
)

## S4 method for signature 'methylRawList'
unite(
  object,
  destrand = FALSE,
  min.per.group = NULL,
  chunk.size = 1e+06,
  mc.cores = 1,
  save.db = FALSE,
  ...
)

## S4 method for signature 'methylRawListDB'
unite(
  object,
  destrand = FALSE,
  min.per.group = NULL,
  chunk.size = 1e+06,
  mc.cores = 1,
  save.db = TRUE,
  ...
)

Arguments

Value

a methylBase or methylBaseDB object depending on input

Details

The parameter chunk.size is only used when working with methylRawDB or methylRawListDB objects, as they are read in chunk by chunk to enable processing large-sized objects which are stored as flat file database. Per default the chunk.size is set to 1M rows, which should work for most systems. If you encounter memory problems or have a high amount of memory available feel free to adjust the chunk.size.

The parameter save.db is per default TRUE for methylDB objects as methylRawListDB, while being per default FALSE for methylRawList. If you wish to save the result of an in-memory-calculation as flat file database or if the size of the database allows the calculation in-memory, then you might change the value of this parameter.

Examples

data(methylKit)
 ## Following 
 my.methylBase=unite(methylRawList.obj) 
 my.methylBase=unite(methylRawList.obj,destrand=TRUE)

Description

update methylKit objects

The method updates object from earlier versions (<v0.9.1) to latest object.

Usage

updateMethObject(object)

Arguments

Value

methylRaw,methylDiff,methylBase or methylRawList object

@export @docType methods @rdname updateMethObject